Ragas
Use Ragas and Milvus or Zilliz Cloud to evaluate your RAG and GenAI applications
Use this integration for FreeWhat is Ragas?
Ragas is a framework designed to evaluate Retrieval Augmented Generation (RAG) pipelines. These pipelines are a subset of large language model (LLM) applications that leverage external data to enhance the context and responses generated by the LLM.
Ragas provides tools for assessing the answer quality of RAG systems by focusing on various metrics such as faithfulness, answer relevance, context precision, and more. This framework supports generating synthetic test datasets, monitoring RAG applications in production, and integrating with various AI tools and platforms like LangChain, LlamaIndex, Milvus, and Zilliz Cloud (the managed Milvus). Ragas aims to simplify and quantify the evaluation process for RAG pipelines to improve their effectiveness and reliability.
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Benefits of the Ragas and Milvus/Zilliz Integration
Milvus and Zilliz Cloud vector databases are pivotal infrastructure components for building RAG applications. By integrating Ragas with Milvus and Zilliz Cloud, developers can efficiently monitor, evaluate, and refine their RAG pipelines. This integration also equips developers with the methodologies and tools needed to maintain high-quality, effective RAG systems.
This integration provides the following key benefits for developers:
Enhanced RAG Evaluation for Production-Ready Applications: Milvus and Zilliz Cloud are widely used vector databases for enterprise-grade applications capable of handling billion-scale vectors. Integrating Milvus/Zilliz with Ragas allows for a fast and comprehensive assessment of RAG applications' performance and accuracy on large datasets for real-world use cases. This integration also ensures that the evaluation process remains efficient and effective as the data grows, enabling developers to build robust, production-ready RAG systems.
Streamlined RAG Development and Evaluation: Milvus offers horizontal scalability and high reliability, allowing developers to focus on building and refining their applications without worrying about infrastructure disruptions. Zilliz Cloud, a managed Milvus service, further simplifies the process by handling the operational complexities of managing a vector database and providing enhanced enterprise readiness. The integration of Milvus/Zilliz with Ragas enables developers to evaluate their RAG applications' performance over time with minimal coding effort. They can easily identify and address issues such as hallucinations in generated answers, iteratively improving their applications to maintain high standards of quality and reliability.
By leveraging the combined strengths of Ragas and Milvus/Zilliz, developers can build, evaluate, and optimize high-performing RAG applications more effectively. This integration ensures robust and reliable systems that provide high-quality answers with massive-scale knowledge bases, ultimately leading to better user experience from AI applications.
How the Ragas and Milvus/Zilliz Integration Works
Developers can assess the precision and recall of contextual information retrieved from Milvus or Zilliz Cloud and evaluate the faithfulness and relevance of content generated by the LLM during the generation stage. Ragas will subsequently compute a weighted score to measure the overall answer quality of your RAG systems.
The RAG and Ragas Evaluation process works as follows:
How Ragas and Zilliz Cloud work together
How to Use Ragas with Milvus/Zilliz Cloud
Papar | [2309.15217] RAGAS: Automated Evaluation of Retrieval Augmented Generation
Milvus Documentation | How to Use Ragas to Evaluate RAG Built upon Milvus
Blog | RAG Evaluation Using Ragas
Discord | Join the Milvus Discord Community with AI Developers